


'''simple docstring'''


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_						:  List[str]				  =						logging.get_logger(__name__)







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Any							= DPTConfig()

				if "large" in checkpoint_url:
								_SCREAMING_SNAKE_CASE					:			List[str]							= 1024
								_SCREAMING_SNAKE_CASE					:			Tuple							= 4096
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 24
								_SCREAMING_SNAKE_CASE					:			int							= 16
								_SCREAMING_SNAKE_CASE					:			Dict							= [5, 11, 17, 23]
								_SCREAMING_SNAKE_CASE					:			Dict							= [256, 512, 1024, 1024]
								_SCREAMING_SNAKE_CASE					:			str							= (1, 384, 384)

				if "ade" in checkpoint_url:
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= True

								_SCREAMING_SNAKE_CASE					:			int							= 150
								_SCREAMING_SNAKE_CASE					:			List[Any]							= """huggingface/label-files"""
								_SCREAMING_SNAKE_CASE					:			Tuple							= """ade20k-id2label.json"""
								_SCREAMING_SNAKE_CASE					:			str							= json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, repo_type="""dataset"""	)	)				, """r"""	)	)
								_SCREAMING_SNAKE_CASE					:			Tuple							= {int(SCREAMING_SNAKE_CASE__	): v for k, v in idalabel.items()}
								_SCREAMING_SNAKE_CASE					:			Tuple							= idalabel
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= {v: k for k, v in idalabel.items()}
								_SCREAMING_SNAKE_CASE					:			List[Any]							= [1, 150, 480, 480]

				return config, expected_shape







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[Any]							= ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
				for k in ignore_keys:
								state_dict.pop(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if (
				    "pretrained.model" in name
				    and "cls_token" not in name
				    and "pos_embed" not in name
				    and "patch_embed" not in name
				):
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= name.replace("""pretrained.model"""				, """dpt.encoder"""	)
				if "pretrained.model" in name:
								_SCREAMING_SNAKE_CASE					:			List[str]							= name.replace("""pretrained.model"""				, """dpt.embeddings"""	)
				if "patch_embed" in name:
								_SCREAMING_SNAKE_CASE					:			Tuple							= name.replace("""patch_embed"""				, """patch_embeddings"""	)
				if "pos_embed" in name:
								_SCREAMING_SNAKE_CASE					:			Tuple							= name.replace("""pos_embed"""				, """position_embeddings"""	)
				if "attn.proj" in name:
								_SCREAMING_SNAKE_CASE					:			List[Any]							= name.replace("""attn.proj"""				, """attention.output.dense"""	)
				if "proj" in name and "project" not in name:
								_SCREAMING_SNAKE_CASE					:			Tuple							= name.replace("""proj"""				, """projection"""	)
				if "blocks" in name:
								_SCREAMING_SNAKE_CASE					:			Any							= name.replace("""blocks"""				, """layer"""	)
				if "mlp.fc1" in name:
								_SCREAMING_SNAKE_CASE					:			str							= name.replace("""mlp.fc1"""				, """intermediate.dense"""	)
				if "mlp.fc2" in name:
								_SCREAMING_SNAKE_CASE					:			Any							= name.replace("""mlp.fc2"""				, """output.dense"""	)
				if "norm1" in name:
								_SCREAMING_SNAKE_CASE					:			List[Any]							= name.replace("""norm1"""				, """layernorm_before"""	)
				if "norm2" in name:
								_SCREAMING_SNAKE_CASE					:			List[str]							= name.replace("""norm2"""				, """layernorm_after"""	)
				if "scratch.output_conv" in name:
								_SCREAMING_SNAKE_CASE					:			str							= name.replace("""scratch.output_conv"""				, """head"""	)
				if "scratch" in name:
								_SCREAMING_SNAKE_CASE					:			Tuple							= name.replace("""scratch"""				, """neck"""	)
				if "layer1_rn" in name:
								_SCREAMING_SNAKE_CASE					:			List[Any]							= name.replace("""layer1_rn"""				, """convs.0"""	)
				if "layer2_rn" in name:
								_SCREAMING_SNAKE_CASE					:			Dict							= name.replace("""layer2_rn"""				, """convs.1"""	)
				if "layer3_rn" in name:
								_SCREAMING_SNAKE_CASE					:			str							= name.replace("""layer3_rn"""				, """convs.2"""	)
				if "layer4_rn" in name:
								_SCREAMING_SNAKE_CASE					:			int							= name.replace("""layer4_rn"""				, """convs.3"""	)
				if "refinenet" in name:
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= int(name[len("""neck.refinenet"""	) : len("""neck.refinenet"""	) + 1]	)
								# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= name.replace(f"""refinenet{layer_idx}"""				, f"""fusion_stage.layers.{abs(layer_idx-4	)}"""	)
				if "out_conv" in name:
								_SCREAMING_SNAKE_CASE					:			List[str]							= name.replace("""out_conv"""				, """projection"""	)
				if "resConfUnit1" in name:
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= name.replace("""resConfUnit1"""				, """residual_layer1"""	)
				if "resConfUnit2" in name:
								_SCREAMING_SNAKE_CASE					:			int							= name.replace("""resConfUnit2"""				, """residual_layer2"""	)
				if "conv1" in name:
								_SCREAMING_SNAKE_CASE					:			List[str]							= name.replace("""conv1"""				, """convolution1"""	)
				if "conv2" in name:
								_SCREAMING_SNAKE_CASE					:			int							= name.replace("""conv2"""				, """convolution2"""	)
				# readout blocks
				if "pretrained.act_postprocess1.0.project.0" in name:
								_SCREAMING_SNAKE_CASE					:			Tuple							= name.replace("""pretrained.act_postprocess1.0.project.0"""				, """neck.reassemble_stage.readout_projects.0.0"""	)
				if "pretrained.act_postprocess2.0.project.0" in name:
								_SCREAMING_SNAKE_CASE					:			List[str]							= name.replace("""pretrained.act_postprocess2.0.project.0"""				, """neck.reassemble_stage.readout_projects.1.0"""	)
				if "pretrained.act_postprocess3.0.project.0" in name:
								_SCREAMING_SNAKE_CASE					:			Dict							= name.replace("""pretrained.act_postprocess3.0.project.0"""				, """neck.reassemble_stage.readout_projects.2.0"""	)
				if "pretrained.act_postprocess4.0.project.0" in name:
								_SCREAMING_SNAKE_CASE					:			str							= name.replace("""pretrained.act_postprocess4.0.project.0"""				, """neck.reassemble_stage.readout_projects.3.0"""	)
				# resize blocks
				if "pretrained.act_postprocess1.3" in name:
								_SCREAMING_SNAKE_CASE					:			List[Any]							= name.replace("""pretrained.act_postprocess1.3"""				, """neck.reassemble_stage.layers.0.projection"""	)
				if "pretrained.act_postprocess1.4" in name:
								_SCREAMING_SNAKE_CASE					:			Tuple							= name.replace("""pretrained.act_postprocess1.4"""				, """neck.reassemble_stage.layers.0.resize"""	)
				if "pretrained.act_postprocess2.3" in name:
								_SCREAMING_SNAKE_CASE					:			Any							= name.replace("""pretrained.act_postprocess2.3"""				, """neck.reassemble_stage.layers.1.projection"""	)
				if "pretrained.act_postprocess2.4" in name:
								_SCREAMING_SNAKE_CASE					:			List[str]							= name.replace("""pretrained.act_postprocess2.4"""				, """neck.reassemble_stage.layers.1.resize"""	)
				if "pretrained.act_postprocess3.3" in name:
								_SCREAMING_SNAKE_CASE					:			List[str]							= name.replace("""pretrained.act_postprocess3.3"""				, """neck.reassemble_stage.layers.2.projection"""	)
				if "pretrained.act_postprocess4.3" in name:
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= name.replace("""pretrained.act_postprocess4.3"""				, """neck.reassemble_stage.layers.3.projection"""	)
				if "pretrained.act_postprocess4.4" in name:
								_SCREAMING_SNAKE_CASE					:			str							= name.replace("""pretrained.act_postprocess4.4"""				, """neck.reassemble_stage.layers.3.resize"""	)
				if "pretrained" in name:
								_SCREAMING_SNAKE_CASE					:			List[str]							= name.replace("""pretrained"""				, """dpt"""	)
				if "bn" in name:
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= name.replace("""bn"""				, """batch_norm"""	)
				if "head" in name:
								_SCREAMING_SNAKE_CASE					:			Any							= name.replace("""head"""				, """head.head"""	)
				if "encoder.norm" in name:
								_SCREAMING_SNAKE_CASE					:			List[Any]							= name.replace("""encoder.norm"""				, """layernorm"""	)
				if "auxlayer" in name:
								_SCREAMING_SNAKE_CASE					:			int							= name.replace("""auxlayer"""				, """auxiliary_head.head"""	)

				return name







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				for i in range(config.num_hidden_layers	):
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""	)
								_SCREAMING_SNAKE_CASE					:			Any							= state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias"""	)
								# next, add query, keys and values (in that order) to the state dict
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= in_proj_weight[: config.hidden_size, :]
								_SCREAMING_SNAKE_CASE					:			str							= in_proj_bias[: config.hidden_size]
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								_SCREAMING_SNAKE_CASE					:			int							= in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								_SCREAMING_SNAKE_CASE					:			List[Any]							= in_proj_weight[
								    -config.hidden_size :, :
								]
								_SCREAMING_SNAKE_CASE					:			List[Any]							= in_proj_bias[-config.hidden_size :]







def      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			int							= """http://images.cocodataset.org/val2017/000000039769.jpg"""
				_SCREAMING_SNAKE_CASE					:			Tuple							= Image.open(requests.get(SCREAMING_SNAKE_CASE__				, stream=SCREAMING_SNAKE_CASE__	).raw	)
				return im







@torch.no_grad()
def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Optional[int]							= get_dpt_config(SCREAMING_SNAKE_CASE__	)
				# load original state_dict from URL
				_SCREAMING_SNAKE_CASE					:			Dict							= torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__				, map_location="""cpu"""	)
				# remove certain keys
				remove_ignore_keys_(SCREAMING_SNAKE_CASE__	)
				# rename keys
				for key in state_dict.copy().keys():
								_SCREAMING_SNAKE_CASE					:			Any							= state_dict.pop(SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= val
				# read in qkv matrices
				read_in_q_k_v(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				# load HuggingFace model
				_SCREAMING_SNAKE_CASE					:			Dict							= DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE__	) if """ade""" in checkpoint_url else DPTForDepthEstimation(SCREAMING_SNAKE_CASE__	)
				model.load_state_dict(SCREAMING_SNAKE_CASE__	)
				model.eval()

				# Check outputs on an image
				_SCREAMING_SNAKE_CASE					:			Tuple							= 480 if """ade""" in checkpoint_url else 384
				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= DPTImageProcessor(size=SCREAMING_SNAKE_CASE__	)

				_SCREAMING_SNAKE_CASE					:			List[str]							= prepare_img()
				_SCREAMING_SNAKE_CASE					:			Tuple							= image_processor(SCREAMING_SNAKE_CASE__				, return_tensors="""pt"""	)

				# forward pass
				_SCREAMING_SNAKE_CASE					:			int							= model(**SCREAMING_SNAKE_CASE__	).logits if """ade""" in checkpoint_url else model(**SCREAMING_SNAKE_CASE__	).predicted_depth

				# Assert logits
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]	)
				if "ade" in checkpoint_url:
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]	)
				assert outputs.shape == torch.Size(SCREAMING_SNAKE_CASE__	)
				assert (
				    torch.allclose(outputs[0, 0, :3, :3]				, SCREAMING_SNAKE_CASE__				, atol=1E-4	)
				    if "ade" in checkpoint_url
				    else torch.allclose(outputs[0, :3, :3]				, SCREAMING_SNAKE_CASE__	)
				)

				Path(SCREAMING_SNAKE_CASE__	).mkdir(exist_ok=SCREAMING_SNAKE_CASE__	)
				print(f"""Saving model to {pytorch_dump_folder_path}"""	)
				model.save_pretrained(SCREAMING_SNAKE_CASE__	)
				print(f"""Saving image processor to {pytorch_dump_folder_path}"""	)
				image_processor.save_pretrained(SCREAMING_SNAKE_CASE__	)

				if push_to_hub:
								print("""Pushing model to hub..."""	)
								model.push_to_hub(
								    repo_path_or_name=Path(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)				, organization="""nielsr"""				, commit_message="""Add model"""				, use_temp_dir=SCREAMING_SNAKE_CASE__				, )
								image_processor.push_to_hub(
								    repo_path_or_name=Path(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)				, organization="""nielsr"""				, commit_message="""Add image processor"""				, use_temp_dir=SCREAMING_SNAKE_CASE__				, )


if __name__ == "__main__":
				UpperCAmelCase_						:  List[str]				  =						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--checkpoint_url',
				    default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
				    type=str,
				    help='URL of the original DPT checkpoint you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path',
				    default=None,
				    type=str,
				    required=True,
				    help='Path to the output PyTorch model directory.',
				)
				parser.add_argument(
				    '--push_to_hub',
				    action='store_true',
				)
				parser.add_argument(
				    '--model_name',
				    default='dpt-large',
				    type=str,
				    help='Name of the model, in case you\'re pushing to the hub.',
				)

				UpperCAmelCase_						:  List[Any]				  =						parser.parse_args()
				convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)








'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class        lowercase__    (		_snake_case	,	unittest.TestCase   ):





							'''simple docstring'''


							A_				: Dict       =    KandinskyVaaPipeline
							A_				: List[str]       =    [
							    """image_embeds""",
							    """negative_image_embeds""",
							]
							A_				: Optional[int]       =    ["""image_embeds""", """negative_image_embeds"""]
							A_				: Optional[Any]       =    [
							    """generator""",
							    """height""",
							    """width""",
							    """latents""",
							    """guidance_scale""",
							    """num_inference_steps""",
							    """return_dict""",
							    """guidance_scale""",
							    """num_images_per_prompt""",
							    """output_type""",
							    """return_dict""",
							]
							A_				: Dict       =    False






							@property
							def UpperCAmelCase_      (       self    ):
											return 32






							@property
							def UpperCAmelCase_      (       self    ):
											return 32






							@property
							def UpperCAmelCase_      (       self    ):
											return self.time_input_dim






							@property
							def UpperCAmelCase_      (       self    ):
											return self.time_input_dim * 4






							@property
							def UpperCAmelCase_      (       self    ):
											return 100






							@property
							def UpperCAmelCase_      (       self    ):
											torch.manual_seed(0    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= {
											    """in_channels""": 4,
											    # Out channels is double in channels because predicts mean and variance
											    """out_channels""": 8,
											    """addition_embed_type""": """image""",
											    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
											    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
											    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
											    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
											    """layers_per_block""": 1,
											    """encoder_hid_dim""": self.text_embedder_hidden_size,
											    """encoder_hid_dim_type""": """image_proj""",
											    """cross_attention_dim""": self.cross_attention_dim,
											    """attention_head_dim""": 4,
											    """resnet_time_scale_shift""": """scale_shift""",
											    """class_embed_type""": None,
											}

											_SCREAMING_SNAKE_CASE					:			Any							= UNetaDConditionModel(**__snake_case    )
											return model






							@property
							def UpperCAmelCase_      (       self    ):
											return {
											    "block_out_channels": [32, 64],
											    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
											    "in_channels": 3,
											    "latent_channels": 4,
											    "layers_per_block": 1,
											    "norm_num_groups": 8,
											    "norm_type": "spatial",
											    "num_vq_embeddings": 12,
											    "out_channels": 3,
											    "up_block_types": [
											        "AttnUpDecoderBlock2D",
											        "UpDecoderBlock2D",
											    ],
											    "vq_embed_dim": 4,
											}






							@property
							def UpperCAmelCase_      (       self    ):
											torch.manual_seed(0    )
											_SCREAMING_SNAKE_CASE					:			Tuple							= VQModel(**self.dummy_movq_kwargs    )
											return model






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			int							= self.dummy_unet
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.dummy_movq

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= DDIMScheduler(
											    num_train_timesteps=1000       ,   beta_schedule="""linear"""       ,   beta_start=0.00085       ,   beta_end=0.012       ,   clip_sample=__snake_case       ,   set_alpha_to_one=__snake_case       ,   steps_offset=1       ,   prediction_type="""epsilon"""       ,   thresholding=__snake_case       ,   )

											_SCREAMING_SNAKE_CASE					:			Dict							= {
											    """unet""": unet,
											    """scheduler""": scheduler,
											    """movq""": movq,
											}
											return components






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case=0    ):
											_SCREAMING_SNAKE_CASE					:			int							= floats_tensor((1, self.text_embedder_hidden_size)       ,   rng=random.Random(__snake_case    )    ).to(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= floats_tensor((1, self.text_embedder_hidden_size)       ,   rng=random.Random(seed + 1    )    ).to(
											    __snake_case    )
											if str(__snake_case    ).startswith("""mps"""    ):
															_SCREAMING_SNAKE_CASE					:			str							= torch.manual_seed(__snake_case    )
											else:
															_SCREAMING_SNAKE_CASE					:			Dict							= torch.Generator(device=__snake_case    ).manual_seed(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Tuple							= {
											    """image_embeds""": image_embeds,
											    """negative_image_embeds""": negative_image_embeds,
											    """generator""": generator,
											    """height""": 64,
											    """width""": 64,
											    """guidance_scale""": 4.0,
											    """num_inference_steps""": 2,
											    """output_type""": """np""",
											}
											return inputs






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= """cpu"""

											_SCREAMING_SNAKE_CASE					:			Dict							= self.get_dummy_components()

											_SCREAMING_SNAKE_CASE					:			List[Any]							= self.pipeline_class(**__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= pipe.to(__snake_case    )

											pipe.set_progress_bar_config(disable=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Any							= pipe(**self.get_dummy_inputs(__snake_case    )    )
											_SCREAMING_SNAKE_CASE					:			Tuple							= output.images

											_SCREAMING_SNAKE_CASE					:			Dict							= pipe(
											    **self.get_dummy_inputs(__snake_case    )       ,   return_dict=__snake_case       ,   )[0]

											_SCREAMING_SNAKE_CASE					:			Tuple							= image[0, -3:, -3:, -1]
											_SCREAMING_SNAKE_CASE					:			List[Any]							= image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)

											_SCREAMING_SNAKE_CASE					:			Dict							= np.array(
											    [0.6237976, 1.0, 0.36441332, 1.0, 0.70639634, 0.29877186, 0.85652125, 0.5216843, 0.54454046]    )

											assert (
											    np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
											), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""

											assert (
											    np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2
											), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""





@slow
@require_torch_gpu
class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							def UpperCAmelCase_      (       self    ):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"""    )

											_SCREAMING_SNAKE_CASE					:			List[Any]							= KandinskyVaaPriorPipeline.from_pretrained(
											    """kandinsky-community/kandinsky-2-2-prior"""       ,   torch_dtype=torch.floataa    )
											pipe_prior.to(__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= KandinskyVaaPipeline.from_pretrained(
											    """kandinsky-community/kandinsky-2-2-decoder"""       ,   torch_dtype=torch.floataa    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= pipeline.to(__snake_case    )
											pipeline.set_progress_bar_config(disable=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Dict							= """red cat, 4k photo"""

											_SCREAMING_SNAKE_CASE					:			List[str]							= torch.Generator(device="""cuda"""    ).manual_seed(0    )
											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= pipe_prior(
											    __snake_case       ,   generator=__snake_case       ,   num_inference_steps=5       ,   negative_prompt=""""""       ,   ).to_tuple()

											_SCREAMING_SNAKE_CASE					:			Dict							= torch.Generator(device="""cuda"""    ).manual_seed(0    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= pipeline(
											    image_embeds=__snake_case       ,   negative_image_embeds=__snake_case       ,   generator=__snake_case       ,   num_inference_steps=100       ,   output_type="""np"""       ,   )

											_SCREAMING_SNAKE_CASE					:			List[Any]							= output.images[0]

											assert image.shape == (512, 512, 3)

											assert_mean_pixel_difference(__snake_case       ,   __snake_case    )




